Articles | Volume 9, issue 10
https://doi.org/10.5194/amt-9-5249-2016
https://doi.org/10.5194/amt-9-5249-2016
Research article
 | 
28 Oct 2016
Research article |  | 28 Oct 2016

Assessing the performance of troposphere tomographic modeling using multi-source water vapor data during Hong Kong's rainy season from May to October 2013

Biyan Chen and Zhizhao Liu

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Short summary
A multi-source water vapor tomography model is developed using GPS (Global Positioning System), radiosonde, WVR (water vapor radiometer), NWP (numerical weather prediction), AERONET (AErosol RObotic NETwork) sunphotometer and synoptic stations' data. Results show that the assimilation of multi-source data can increase the quality of the tomographic solution. Evaluation shows that the tomography model is robust during heavy rain conditions, and it can contribute to severe weather forecasting.